{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7765UFHoyGx6"
   },
   "source": [
    "##### Copyright 2019 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "both",
    "colab": {},
    "colab_type": "code",
    "id": "KVtTDrUNyL7x"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "r0_fqL3ayLHX"
   },
   "source": [
    "# Gradient Boosted Trees: Model understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PS6_yKSoyLAl"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/estimator/boosted_trees_model_understanding\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/estimator/boosted_trees_model_understanding.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/estimator/boosted_trees_model_understanding.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dW3r7qVxzqN5"
   },
   "source": [
    "\n",
    "For an end-to-end walkthrough of training a Gradient Boosting model check out the [boosted trees tutorial](./boosted_trees). In this tutorial you will:\n",
    "\n",
    "* Learn how to interpret a Boosted Trees model both *locally* and *globally*\n",
    "* Gain intution for how a Boosted Trees model fits a dataset\n",
    "\n",
    "## How to interpret Boosted Trees models both locally and globally\n",
    "\n",
    "Local interpretability refers to an understanding of a model’s predictions at the individual example level, while global interpretability refers to an understanding of the model as a whole. Such techniques can help machine learning (ML) practitioners detect bias and bugs during the model development stage.\n",
    "\n",
    "For local interpretability, you will learn how to create and visualize per-instance contributions. To distinguish this from feature importances, we refer to these values as directional feature contributions (DFCs).\n",
    "\n",
    "For global interpretability you will retrieve and visualize gain-based feature importances, [permutation feature importances](https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf) and also show aggregated DFCs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eylrTPAN3rJV"
   },
   "source": [
    "## Load the titanic dataset\n",
    "You will be using the titanic dataset, where the (rather morbid) goal is to predict passenger survival, given characteristics such as gender, age, class, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KuhAiPfZ3rJW"
   },
   "outputs": [],
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import clear_output\n",
    "\n",
    "# Load dataset.\n",
    "dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')\n",
    "dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv')\n",
    "y_train = dftrain.pop('survived')\n",
    "y_eval = dfeval.pop('survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sp1ShjJJeyH3"
   },
   "outputs": [],
   "source": [
    "try:\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
    "\n",
    "import tensorflow as tf\n",
    "tf.random.set_seed(123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3ioodHdVJVdA"
   },
   "source": [
    "For a description of the features, please review the prior tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "krkRHuMp3rJn"
   },
   "source": [
    "## Create feature columns, input_fn, and the train the estimator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JiJ6K3hr1lXW"
   },
   "source": [
    "### Preprocess the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "udMytRJC05oW"
   },
   "source": [
    "Create the feature columns, using the original numeric columns as is and one-hot-encoding categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "upaNWxcF3rJn"
   },
   "outputs": [],
   "source": [
    "fc = tf.feature_column\n",
    "CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck',\n",
    "                       'embark_town', 'alone']\n",
    "NUMERIC_COLUMNS = ['age', 'fare']\n",
    "\n",
    "def one_hot_cat_column(feature_name, vocab):\n",
    "  return fc.indicator_column(\n",
    "      fc.categorical_column_with_vocabulary_list(feature_name,\n",
    "                                                 vocab))\n",
    "feature_columns = []\n",
    "for feature_name in CATEGORICAL_COLUMNS:\n",
    "  # Need to one-hot encode categorical features.\n",
    "  vocabulary = dftrain[feature_name].unique()\n",
    "  feature_columns.append(one_hot_cat_column(feature_name, vocabulary))\n",
    "\n",
    "for feature_name in NUMERIC_COLUMNS:\n",
    "  feature_columns.append(fc.numeric_column(feature_name,\n",
    "                                           dtype=tf.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9rTefnXe1n0v"
   },
   "source": [
    "### Build the input pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-UOlROp33rJo"
   },
   "source": [
    "Create the input functions using the `from_tensor_slices` method in the [`tf.data`](https://www.tensorflow.org/api_docs/python/tf/data) API to read in data directly from Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9dquwCQB3rJp"
   },
   "outputs": [],
   "source": [
    "# Use entire batch since this is such a small dataset.\n",
    "NUM_EXAMPLES = len(y_train)\n",
    "\n",
    "def make_input_fn(X, y, n_epochs=None, shuffle=True):\n",
    "  def input_fn():\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((X.to_dict(orient='list'), y))\n",
    "    if shuffle:\n",
    "      dataset = dataset.shuffle(NUM_EXAMPLES)\n",
    "    # For training, cycle thru dataset as many times as need (n_epochs=None).\n",
    "    dataset = (dataset\n",
    "      .repeat(n_epochs)\n",
    "      .batch(NUM_EXAMPLES))\n",
    "    return dataset\n",
    "  return input_fn\n",
    "\n",
    "# Training and evaluation input functions.\n",
    "train_input_fn = make_input_fn(dftrain, y_train)\n",
    "eval_input_fn = make_input_fn(dfeval, y_eval, shuffle=False, n_epochs=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HttfNNlN3rJr"
   },
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 390
    },
    "colab_type": "code",
    "id": "tgEzMtlw3rJu",
    "outputId": "7e3cb2ae-abc8-425f-ad38-cfb8cdf58fa5"
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.806818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy_baseline</th>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auc</th>\n",
       "      <td>0.866942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auc_precision_recall</th>\n",
       "      <td>0.845483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_loss</th>\n",
       "      <td>0.421330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>global_step</th>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label/mean</th>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loss</th>\n",
       "      <td>0.421330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.755319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prediction/mean</th>\n",
       "      <td>0.385978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.717172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                               0\n",
       "accuracy                0.806818\n",
       "accuracy_baseline       0.625000\n",
       "auc                     0.866942\n",
       "auc_precision_recall    0.845483\n",
       "average_loss            0.421330\n",
       "global_step           100.000000\n",
       "label/mean              0.375000\n",
       "loss                    0.421330\n",
       "precision               0.755319\n",
       "prediction/mean         0.385978\n",
       "recall                  0.717172"
      ]
     },
     "execution_count": 0,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "  'n_trees': 50,\n",
    "  'max_depth': 3,\n",
    "  'n_batches_per_layer': 1,\n",
    "  # You must enable center_bias = True to get DFCs. This will force the model to\n",
    "  # make an initial prediction before using any features (e.g. use the mean of\n",
    "  # the training labels for regression or log odds for classification when\n",
    "  # using cross entropy loss).\n",
    "  'center_bias': True\n",
    "}\n",
    "\n",
    "est = tf.estimator.BoostedTreesClassifier(feature_columns, **params)\n",
    "# Train model.\n",
    "est.train(train_input_fn, max_steps=100)\n",
    "\n",
    "# Evaluation.\n",
    "results = est.evaluate(eval_input_fn)\n",
    "clear_output()\n",
    "pd.Series(results).to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JgAz3jDa_tRA"
   },
   "source": [
    "For performance reasons, when your data fits in memory, we recommend use the `boosted_trees_classifier_train_in_memory` function. However if training time is not of a concern or if you have a very large dataset and want to do distributed training, use the `tf.estimator.BoostedTrees` API shown above.\n",
    "\n",
    "\n",
    "When using this method, you should not batch your input data, as the method operates on the entire dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 54
    },
    "colab_type": "code",
    "id": "y7ztzoSk_vjY",
    "outputId": "028523bf-556f-4beb-f9eb-1e476aa4ddaa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'accuracy_baseline': 0.625, 'loss': 0.41725963, 'auc': 0.86810529, 'label/mean': 0.375, 'average_loss': 0.41725963, 'accuracy': 0.80681819, 'global_step': 153, 'precision': 0.75531918, 'prediction/mean': 0.38610166, 'recall': 0.71717173, 'auc_precision_recall': 0.8542586}\n"
     ]
    }
   ],
   "source": [
    "in_memory_params = dict(params)\n",
    "in_memory_params['n_batches_per_layer'] = 1\n",
    "# In-memory input_fn does not use batching.\n",
    "def make_inmemory_train_input_fn(X, y):\n",
    "  y = np.expand_dims(y, axis=1)\n",
    "  def input_fn():\n",
    "    return dict(X), y\n",
    "  return input_fn\n",
    "train_input_fn = make_inmemory_train_input_fn(dftrain, y_train)\n",
    "\n",
    "# Train the model.\n",
    "est = tf.estimator.BoostedTreesClassifier(\n",
    "    feature_columns, \n",
    "    train_in_memory=True, \n",
    "    **in_memory_params)\n",
    "\n",
    "est.train(train_input_fn)\n",
    "print(est.evaluate(eval_input_fn))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TSZYqNcRuczV"
   },
   "source": [
    "## Model interpretation and plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BjcfLiI3uczW"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns_colors = sns.color_palette('colorblind')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ywTtbBvBuczY"
   },
   "source": [
    "## Local interpretability\n",
    "Next you will output the directional feature contributions (DFCs) to explain individual predictions using the approach outlined in [Palczewska et al](https://arxiv.org/pdf/1312.1121.pdf) and by Saabas in [Interpreting Random Forests](http://blog.datadive.net/interpreting-random-forests/) (this method is also available in scikit-learn for Random Forests in the [`treeinterpreter`](https://github.com/andosa/treeinterpreter) package). The DFCs are generated with:\n",
    "\n",
    "`pred_dicts = list(est.experimental_predict_with_explanations(pred_input_fn))`\n",
    "\n",
    "(Note: The method is named experimental as we may modify the API before dropping the experimental prefix.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TIL93B4sDRqE"
   },
   "outputs": [],
   "source": [
    "pred_dicts = list(est.experimental_predict_with_explanations(eval_input_fn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 328
    },
    "colab_type": "code",
    "id": "tDPoRx_ZaY1E",
    "outputId": "72c9a915-e8c9-4104-f737-3282c6ff5960"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>264.0</td>\n",
       "      <td>-0.025285</td>\n",
       "      <td>0.085606</td>\n",
       "      <td>-0.172007</td>\n",
       "      <td>-0.075237</td>\n",
       "      <td>-0.051290</td>\n",
       "      <td>0.011908</td>\n",
       "      <td>0.466216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>264.0</td>\n",
       "      <td>0.008138</td>\n",
       "      <td>0.108147</td>\n",
       "      <td>-0.097388</td>\n",
       "      <td>-0.074132</td>\n",
       "      <td>-0.072698</td>\n",
       "      <td>0.138499</td>\n",
       "      <td>0.197285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <td>264.0</td>\n",
       "      <td>0.017317</td>\n",
       "      <td>0.093436</td>\n",
       "      <td>-0.075741</td>\n",
       "      <td>-0.045486</td>\n",
       "      <td>-0.044461</td>\n",
       "      <td>0.035060</td>\n",
       "      <td>0.248212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deck</th>\n",
       "      <td>264.0</td>\n",
       "      <td>-0.016094</td>\n",
       "      <td>0.033232</td>\n",
       "      <td>-0.096513</td>\n",
       "      <td>-0.042359</td>\n",
       "      <td>-0.026754</td>\n",
       "      <td>0.005053</td>\n",
       "      <td>0.205399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare</th>\n",
       "      <td>264.0</td>\n",
       "      <td>0.016471</td>\n",
       "      <td>0.089597</td>\n",
       "      <td>-0.330205</td>\n",
       "      <td>-0.031050</td>\n",
       "      <td>-0.011403</td>\n",
       "      <td>0.050212</td>\n",
       "      <td>0.229989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>embark_town</th>\n",
       "      <td>264.0</td>\n",
       "      <td>-0.007320</td>\n",
       "      <td>0.027672</td>\n",
       "      <td>-0.053676</td>\n",
       "      <td>-0.015208</td>\n",
       "      <td>-0.014375</td>\n",
       "      <td>-0.003016</td>\n",
       "      <td>0.068483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_siblings_spouses</th>\n",
       "      <td>264.0</td>\n",
       "      <td>0.003973</td>\n",
       "      <td>0.027914</td>\n",
       "      <td>-0.144680</td>\n",
       "      <td>0.003582</td>\n",
       "      <td>0.004825</td>\n",
       "      <td>0.006412</td>\n",
       "      <td>0.138123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parch</th>\n",
       "      <td>264.0</td>\n",
       "      <td>0.001342</td>\n",
       "      <td>0.007995</td>\n",
       "      <td>-0.062833</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.000510</td>\n",
       "      <td>0.002834</td>\n",
       "      <td>0.048722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alone</th>\n",
       "      <td>264.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count      mean       std  ...       50%       75%       max\n",
       "age                 264.0 -0.025285  0.085606  ... -0.051290  0.011908  0.466216\n",
       "sex                 264.0  0.008138  0.108147  ... -0.072698  0.138499  0.197285\n",
       "class               264.0  0.017317  0.093436  ... -0.044461  0.035060  0.248212\n",
       "deck                264.0 -0.016094  0.033232  ... -0.026754  0.005053  0.205399\n",
       "fare                264.0  0.016471  0.089597  ... -0.011403  0.050212  0.229989\n",
       "embark_town         264.0 -0.007320  0.027672  ... -0.014375 -0.003016  0.068483\n",
       "n_siblings_spouses  264.0  0.003973  0.027914  ...  0.004825  0.006412  0.138123\n",
       "parch               264.0  0.001342  0.007995  ...  0.000510  0.002834  0.048722\n",
       "alone               264.0  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
       "\n",
       "[9 rows x 8 columns]"
      ]
     },
     "execution_count": 0,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create DFC Pandas dataframe.\n",
    "labels = y_eval.values\n",
    "probs = pd.Series([pred['probabilities'][1] for pred in pred_dicts])\n",
    "df_dfc = pd.DataFrame([pred['dfc'] for pred in pred_dicts])\n",
    "df_dfc.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EUKSaVoraY1C"
   },
   "source": [
    "A nice property of DFCs is that the sum of the contributions + the bias is equal to the prediction for a given example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Hd9VuizRaY1H"
   },
   "outputs": [],
   "source": [
    "# Sum of DFCs + bias == probabality.\n",
    "bias = pred_dicts[0]['bias']\n",
    "dfc_prob = df_dfc.sum(axis=1) + bias\n",
    "np.testing.assert_almost_equal(dfc_prob.values,\n",
    "                               probs.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tx5p4vEhuczg"
   },
   "source": [
    "Plot DFCs for an individual passenger. Let's make the plot nice by color coding based on the contributions' directionality and add the feature values on figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6z_Tq1Pquczj"
   },
   "outputs": [],
   "source": [
    "# Boilerplate code for plotting :)\n",
    "def _get_color(value):\n",
    "    \"\"\"To make positive DFCs plot green, negative DFCs plot red.\"\"\"\n",
    "    green, red = sns.color_palette()[2:4]\n",
    "    if value >= 0: return green\n",
    "    return red\n",
    "\n",
    "def _add_feature_values(feature_values, ax):\n",
    "    \"\"\"Display feature's values on left of plot.\"\"\"\n",
    "    x_coord = ax.get_xlim()[0]\n",
    "    OFFSET = 0.15\n",
    "    for y_coord, (feat_name, feat_val) in enumerate(feature_values.items()):\n",
    "        t = plt.text(x_coord, y_coord - OFFSET, '{}'.format(feat_val), size=12)\n",
    "        t.set_bbox(dict(facecolor='white', alpha=0.5))\n",
    "    from matplotlib.font_manager import FontProperties\n",
    "    font = FontProperties()\n",
    "    font.set_weight('bold')\n",
    "    t = plt.text(x_coord, y_coord + 1 - OFFSET, 'feature\\nvalue',\n",
    "    fontproperties=font, size=12)\n",
    "\n",
    "def plot_example(example):\n",
    "  TOP_N = 8 # View top 8 features.\n",
    "  sorted_ix = example.abs().sort_values()[-TOP_N:].index  # Sort by magnitude.\n",
    "  example = example[sorted_ix]\n",
    "  colors = example.map(_get_color).tolist()\n",
    "  ax = example.to_frame().plot(kind='barh',\n",
    "                          color=[colors],\n",
    "                          legend=None,\n",
    "                          alpha=0.75,\n",
    "                          figsize=(10,6))\n",
    "  ax.grid(False, axis='y')\n",
    "  ax.set_yticklabels(ax.get_yticklabels(), size=14)\n",
    "\n",
    "  # Add feature values.\n",
    "  _add_feature_values(dfeval.iloc[ID][sorted_ix], ax)\n",
    "  return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 444
    },
    "colab_type": "code",
    "id": "Ht1P2-1euczk",
    "outputId": "c666aea0-a8ee-4de1-c8fa-f19cdee75e88"
   },
   "outputs": [
    {
     "data": {
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AgAG2etK7Ee72k0Jvu1cC6+XlRbFixdi8efM94/f09MTNzY2zZ8+mSfALFiyYJrGNjIy8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\nInai5FpERERExE7+HwT8XqNhyV+YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f08ab9b8490>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results.\n",
    "ID = 182\n",
    "example = df_dfc.iloc[ID]  # Choose ith example from evaluation set.\n",
    "TOP_N = 8  # View top 8 features.\n",
    "sorted_ix = example.abs().sort_values()[-TOP_N:].index\n",
    "ax = plot_example(example)\n",
    "ax.set_title('Feature contributions for example {}\\n pred: {:1.2f}; label: {}'.format(ID, probs[ID], labels[ID]))\n",
    "ax.set_xlabel('Contribution to predicted probability', size=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aPXgWyFcfzAc"
   },
   "source": [
    "The larger magnitude contributions have a larger impact on the model's prediction. Negative contributions indicate the feature value for this given example reduced the model's prediction, while positive values contribute an increase in the prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0swvlkZFaY1Z"
   },
   "source": [
    "You can also plot the example's DFCs compare with the entire distribution using a voilin plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zo7rNd1v_5e2"
   },
   "outputs": [],
   "source": [
    "# Boilerplate plotting code.\n",
    "def dist_violin_plot(df_dfc, ID):\n",
    "  # Initialize plot.\n",
    "  fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n",
    "\n",
    "  # Create example dataframe.\n",
    "  TOP_N = 8  # View top 8 features.\n",
    "  example = df_dfc.iloc[ID]\n",
    "  ix = example.abs().sort_values()[-TOP_N:].index\n",
    "  example = example[ix]\n",
    "  example_df = example.to_frame(name='dfc')\n",
    "\n",
    "  # Add contributions of entire distribution.\n",
    "  parts=ax.violinplot([df_dfc[w] for w in ix],\n",
    "                 vert=False,\n",
    "                 showextrema=False,\n",
    "                 widths=0.7,\n",
    "                 positions=np.arange(len(ix)))\n",
    "  face_color = sns_colors[0]\n",
    "  alpha = 0.15\n",
    "  for pc in parts['bodies']:\n",
    "      pc.set_facecolor(face_color)\n",
    "      pc.set_alpha(alpha)\n",
    "\n",
    "  # Add feature values.\n",
    "  _add_feature_values(dfeval.iloc[ID][sorted_ix], ax)\n",
    "\n",
    "  # Add local contributions.\n",
    "  ax.scatter(example,\n",
    "              np.arange(example.shape[0]),\n",
    "              color=sns.color_palette()[2],\n",
    "              s=100,\n",
    "              marker=\"s\",\n",
    "              label='contributions for example')\n",
    "\n",
    "  # Legend\n",
    "  # Proxy plot, to show violinplot dist on legend.\n",
    "  ax.plot([0,0], [1,1], label='eval set contributions\\ndistributions',\n",
    "          color=face_color, alpha=alpha, linewidth=10)\n",
    "  legend = ax.legend(loc='lower right', shadow=True, fontsize='x-large',\n",
    "                     frameon=True)\n",
    "  legend.get_frame().set_facecolor('white')\n",
    "\n",
    "  # Format plot.\n",
    "  ax.set_yticks(np.arange(example.shape[0]))\n",
    "  ax.set_yticklabels(example.index)\n",
    "  ax.grid(False, axis='y')\n",
    "  ax.set_xlabel('Contribution to predicted probability', size=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PiLw2tlm_9aK"
   },
   "source": [
    "Plot this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 435
    },
    "colab_type": "code",
    "id": "VkCqraA2uczm",
    "outputId": "71a86715-69c9-4f31-8dc5-383c0018465b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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y5pvvnpsTFEIIIUSpJy23pUyXLt3RNA1d1+nc2Z7Xvm/f/rRu3ZZAIMDXX29l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9AAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f08ab4570d0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_violin_plot(df_dfc, ID)\n",
    "plt.title('Feature contributions for example {}\\n pred: {:1.2f}; label: {}'.format(ID, probs[ID], labels[ID]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TVJFM85SAWVq"
   },
   "source": [
    "Finally, third-party tools, such as [LIME](https://github.com/marcotcr/lime) and [shap](https://github.com/slundberg/shap), can also help understand individual predictions for a model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PnNXH6mZuczr"
   },
   "source": [
    "## Global feature importances\n",
    "\n",
    "Additionally, you might want to understand the model as a whole, rather than studying individual predictions. Below, you will compute and use:\n",
    "\n",
    "* Gain-based feature importances using `est.experimental_feature_importances`\n",
    "* Permutation importances\n",
    "* Aggregate DFCs using `est.experimental_predict_with_explanations`\n",
    "\n",
    "Gain-based feature importances measure the loss change when splitting on a particular feature, while permutation feature importances are computed by evaluating model performance on the evaluation set by shuffling each feature one-by-one and attributing the change in model performance to the shuffled feature.\n",
    "\n",
    "In general, permutation feature importance are preferred to gain-based feature importance, though both methods can be unreliable in situations where potential predictor variables vary in their scale of measurement or their number of categories and when features are correlated ([source](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-307)). Check out [this article](http://explained.ai/rf-importance/index.html) for an in-depth overview and great discussion on different feature importance types."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3ocBcMatuczs"
   },
   "source": [
    "### Gain-based feature importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gMaxCgPbBJ-j"
   },
   "source": [
    "Gain-based feature importances are built into the TensorFlow Boosted Trees estimators using `est.experimental_feature_importances`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 401
    },
    "colab_type": "code",
    "id": "pPTxbAaeuczt",
    "outputId": "2bd9bece-7335-4908-93b1-e8c21894c405"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Qdna2unXrpoYNG+qVV15Rv3791Lx5czVt2lRPP/20oqOj9fvf//6GsYvyJAAAAChtXLUE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f08ac58b790>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = est.experimental_feature_importances(normalize=True)\n",
    "df_imp = pd.Series(importances)\n",
    "\n",
    "# Visualize importances.\n",
    "N = 8\n",
    "ax = (df_imp.iloc[0:N][::-1]\n",
    "    .plot(kind='barh',\n",
    "          color=sns_colors[0],\n",
    "          title='Gain feature importances',\n",
    "          figsize=(10, 6)))\n",
    "ax.grid(False, axis='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GvfAcBeGuczw"
   },
   "source": [
    "### Average absolute DFCs\n",
    "You can also average the absolute values of DFCs to understand impact at a global level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 401
    },
    "colab_type": "code",
    "id": "JkvAWLWLuczx",
    "outputId": "080348ec-2de1-45dc-9c91-9756ded3f517"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VsmVLffjhh3rvvfecY+f36/vrr7/qmWeeUfPmzdWjRw9FRESoV69eN92/tOPPc89zd88t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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f08abd93810>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot.\n",
    "dfc_mean = df_dfc.abs().mean()\n",
    "N = 8\n",
    "sorted_ix = dfc_mean.abs().sort_values()[-N:].index  # Average and sort by absolute.\n",
    "ax = dfc_mean[sorted_ix].plot(kind='barh',\n",
    "                       color=sns_colors[1],\n",
    "                       title='Mean |directional feature contributions|',\n",
    "                       figsize=(10, 6))\n",
    "ax.grid(False, axis='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z0k_DvPLaY1o"
   },
   "source": [
    "You can also see how DFCs vary as a feature value varies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 297
    },
    "colab_type": "code",
    "id": "ZcIfN1IpaY1o",
    "outputId": "abdd429c-8318-4641-aef3-e58e408ac74f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6jWEAlVyMJdNvwdelNQHPmWluteH5d07gZqPFrxoLwwBJiQlIShRBKZcgO1UG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\nCCGEVxR4CCGE8IoCDyGEEF79f73xgK649h0MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f08ab0229d0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FEATURE = 'fare'\n",
    "feature = pd.Series(df_dfc[FEATURE].values, index=dfeval[FEATURE].values).sort_index()\n",
    "ax = sns.regplot(feature.index.values, feature.values, lowess=True)\n",
    "ax.set_ylabel('contribution')\n",
    "ax.set_xlabel(FEATURE)\n",
    "ax.set_xlim(0, 100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lbpG72ULucz0"
   },
   "source": [
    "### Permutation feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 401
    },
    "colab_type": "code",
    "id": "6esOw1VOucz0",
    "outputId": "edf89eaf-e61e-4980-8586-3a0c6eb4f626"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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duFz5+QWhLqNWxcRE1btzvpbQP3vRO3s5nQ61aBEZvPmCNhMAAABqFMENAADA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\nW/CfFsoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0803bab090>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def permutation_importances(est, X_eval, y_eval, metric, features):\n",
    "    \"\"\"Column by column, shuffle values and observe effect on eval set.\n",
    "\n",
    "    source: http://explained.ai/rf-importance/index.html\n",
    "    A similar approach can be done during training. See \"Drop-column importance\"\n",
    "    in the above article.\"\"\"\n",
    "    baseline = metric(est, X_eval, y_eval)\n",
    "    imp = []\n",
    "    for col in features:\n",
    "        save = X_eval[col].copy()\n",
    "        X_eval[col] = np.random.permutation(X_eval[col])\n",
    "        m = metric(est, X_eval, y_eval)\n",
    "        X_eval[col] = save\n",
    "        imp.append(baseline - m)\n",
    "    return np.array(imp)\n",
    "\n",
    "def accuracy_metric(est, X, y):\n",
    "    \"\"\"TensorFlow estimator accuracy.\"\"\"\n",
    "    eval_input_fn = make_input_fn(X,\n",
    "                                  y=y,\n",
    "                                  shuffle=False,\n",
    "                                  n_epochs=1)\n",
    "    return est.evaluate(input_fn=eval_input_fn)['accuracy']\n",
    "features = CATEGORICAL_COLUMNS + NUMERIC_COLUMNS\n",
    "importances = permutation_importances(est, dfeval, y_eval, accuracy_metric,\n",
    "                                      features)\n",
    "df_imp = pd.Series(importances, index=features)\n",
    "\n",
    "sorted_ix = df_imp.abs().sort_values().index\n",
    "ax = df_imp[sorted_ix][-5:].plot(kind='barh', color=sns_colors[2], figsize=(10, 6))\n",
    "ax.grid(False, axis='y')\n",
    "ax.set_title('Permutation feature importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "E236y3pVEzHg"
   },
   "source": [
    "## Visualizing model fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TrcQ-839EzZ6"
   },
   "source": [
    "Lets first simulate/create training data using the following formula:\n",
    "\n",
    "\n",
    "$$z=x* e^{-x^2 - y^2}$$\n",
    "\n",
    "\n",
    "Where \\(z\\) is the dependent variable you are trying to predict and \\(x\\) and \\(y\\) are the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "e8woaj81GGE9"
   },
   "outputs": [],
   "source": [
    "from numpy.random import uniform, seed\n",
    "from matplotlib.mlab import griddata\n",
    "\n",
    "# Create fake data\n",
    "seed(0)\n",
    "npts = 5000\n",
    "x = uniform(-2, 2, npts)\n",
    "y = uniform(-2, 2, npts)\n",
    "z = x*np.exp(-x**2 - y**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GRI3KHfLZsGP"
   },
   "outputs": [],
   "source": [
    "# Prep data for training.\n",
    "df = pd.DataFrame({'x': x, 'y': y, 'z': z})\n",
    "\n",
    "xi = np.linspace(-2.0, 2.0, 200),\n",
    "yi = np.linspace(-2.1, 2.1, 210),\n",
    "xi,yi = np.meshgrid(xi, yi)\n",
    "\n",
    "df_predict = pd.DataFrame({\n",
    "    'x' : xi.flatten(),\n",
    "    'y' : yi.flatten(),\n",
    "})\n",
    "predict_shape = xi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w0JnH4IhZuAb"
   },
   "outputs": [],
   "source": [
    "def plot_contour(x, y, z, **kwargs):\n",
    "  # Grid the data.\n",
    "  plt.figure(figsize=(10, 8))\n",
    "  # Contour the gridded data, plotting dots at the nonuniform data points.\n",
    "  CS = plt.contour(x, y, z, 15, linewidths=0.5, colors='k')\n",
    "  CS = plt.contourf(x, y, z, 15,\n",
    "                    vmax=abs(zi).max(), vmin=-abs(zi).max(), cmap='RdBu_r')\n",
    "  plt.colorbar()  # Draw colorbar.\n",
    "  # Plot data points.\n",
    "  plt.xlim(-2, 2)\n",
    "  plt.ylim(-2, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KF7WsIcYGF_E"
   },
   "source": [
    "You can visualize the function. Redder colors correspond to larger function values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 512
    },
    "colab_type": "code",
    "id": "WrxuqaaXGFOK",
    "outputId": "08121255-36c0-4946-d601-a808b94647a0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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E0LH/0oUMHmJBsyjJMl2O8+C1D6Bs5sS++khlDJdeKyF2K4g7r9TuoyD68RAO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tnXq1G86LqYHnOLp+UJ9phjUFlagh8/ew/G6BjE33YvdEbxWit1hVDpAQLAqHZp8Pn8WDQt4G9i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\nK4JYpNOwXK9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k3eMhCRm4WIKUcOQkqq7Ejb4hL/aEY3hWN0mntyjQm2cCAOfy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8S59QAQd+8STBp7J7Z+9Qaw/RMACH+uy3Lg7TMBxK414VMMisFBEIFMgYR1BQefn8Ghc4W4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\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Z2QpSsb4pk4GVMquEUJOadxiiuCHDSHUun0M2k1WdegVRRQRDPyNQPFQD8wiQgZa+sfCE\nkTzOOpKwgrjpZnh1rZGJD9knw7Ix46oIexQmfsmBaN2R96OP3G/aOk7luZH7zi3EPIh+iwFJntKM\nDk5U1Cp5iD306iwLzdkM6jkxI3R2YK2hcxgnkYEYNmbYfJIKdOdZmpK697RsRb7gVoiUlTIrcgOi\nfeYRahaiPUhSJFaFVOd6etTcdioigoLDhHeoqrzBKx+q5aK4QTKQFpMvym1nZzJw0y3y/mOSPO46\nYlpB7EwGhmFEI4cU4SPPs7LVJ674JSnJQl1X+bwWSFCdXlfHiWiYkQzHzeamHYQWBuQdyufd0IOJ\nB/pQqLWqNzn0TKByoKbtFPfA1NGjkkGFEKgQEl4pFh1Vb50xw+YT1zrEK5eT9L0na2AzAWXrqrfF\nhJqFrAQQDREJCltvGIk1DKCQLyAT4rarOixVrA6ymBbZQcux3AhB2popLhkQki/KbRcWdKyydiUk\nSQ7K8/d9X0kDS3Q9uVY1YF+kZ8XrUyWOTSWmLQ5oK2RkiHScnE5+e3bORW/zBasPIhIGJnnKOdoW\nCfoAkb1pJ+nKAkpWsk2d+UpsTJF9CQ9561cdL+yApEmbqlAjmXO9bccmG2H6TbUAL/staQFJAl4Q\nv8ziFEaoRdB1daquV0RiyXPluwW55YkhOtz0/qo2YqVtWRkQnfgb0paIPpIU/CoyIHH3hVoRIqy9\nLy0LVsqUFq6V7adKhqAo0J2rZyVDhJI1sWOxmIy8OC4zloS7dqNW1mBfq/YPIh4GJHkC9A5c9gAB\nwuuy8aBrsSCH2ZDGDAoFF91OdQaW7K1fdbywA1LHykFAx/MQs3+tysfUAllO9puO/lcchPVZLAZV\nzFlCHda3jqtTV4SUBv1cwS8iLSNPDNHhpvcH2hSkStsEVZ/pkJBy25Lgaa/oo9+9US4XwCBUDVp7\n7X1kWVDMMoxVu00S6M7VsxKMF6lkTRKElCodQ9yziWQVCiypKrFvgxgYMPzIynebD+vXd6JY9IGM\nuiWk3raQtlNwvSJcr1iJMaLLi7TUlR7qe66biraefFUQuOx7GVR0etg2uuNly3XPiAo1gWkCzdne\nftpzea3DOmulYKVMWCmzqu++QBSNI3bv6DWTfcoXXPT08VpYmCYqhI4F73kggp9R5i/bR9l3ZLyF\njz+O5jQwcZ/PhAxkVmKLSrEfHIuHShtBvwCC1zmd0gOHtC04LtK2Vfm7MibVb2xorL2voLLHWvdB\n9Xp6ncyaq9obJozGYZWfV79zPUhB5opcgmDfeH1F2V/tPVAYh+4TAPffYWO1uQ0wTQPDhjWqLSRB\nfOb0m/HhmngZpjS2GdmC1+78bmL9bW4MWMsTACCvFkRL3CRe0a9kfbXUZQKp82Fv6GFikDKoHP5s\nm0wqVTF7q1hIRG4X2srhuEWkNVNhc66HroK+m1QVsrIpOpY+mfuKECfakpKpsRVNpe+0yV8fu276\n/1HnLyNH0iy78nPl9bTBtm2FgTiFgwVv16puCtbNQa4DIHXZ0OrSdm4j/O6NgaDcxNPvVdYVlzhp\nWkVU9lh6r6KOUSG61S5MXpkSYl1yvSJcu7G6lqHAXUr3Fed+6rjMVMep9Ol0BqxNltM56J7bAjCw\nyVMZhiE+BGj3DQmcdL0iN41cVbkaiK/hE7qeCBmFogOyxy2Rva9dWyKOdQpuQLZf3ZIrKggrmxKl\nNiGJH9LN/EsCOqRP5GqtY9bNxm3lE5y/ajyU7wOu48KyNBXGqew24UEjOBTpf3PdPHZTIBOu6tCv\nimVpgVHfSrlL3drGINXAuhRFYVt5Lir3iu2XuZ6FVJ2daU8Cq0+7Zl5onBPpO6AYH9eFp2qxUh2H\n3ntBPF9/R/faj9C5ZmVif7rXfrS5l5QoBjx5qhxUgmKw5PO0nUK3U7KguF4xoDhNQ+VAihLgHUUz\nSZZRGFWDacmKdVKrhcrBn0S2Gu/gZj9TIQrkmlOunouUaQZiu5IiRzpK8Sz5kRESXnB3piy2ScdD\n0e16EqxTqBUPBR/ahYEjHGhVBzjv4DHMyqFMBCyrDiNBHFLaTuHt/2wsu+4U08n7Q9q4ccj/twAA\nHt9JREFU5l6GCXJW/Tus/4jij8rq7KpxTqK5MvF0uiV4lEmpQuwarz8iRzBobdpy0A9+K0SHYRiV\numWOW6wcOAQ8943v8xWnVSFzp8msX0loJpXWzC8SrNJfvuBiwrjhUkIWdvAnVb+Nd3Czn6kQBd8H\n8gUXs66dCtsypRlsUTLtdK1ImXLcmQohEQV3Txg3HMve31AO1q+u1ZekxUxVS8uIHIyrESTNORS5\nhV2Zg1J0INGuGDooeJdtW4Pp5JIDrd+UzmD3UgYJERJKAEjulZb4IxMgr6POTt+vMNcnb66l+5zX\ny5qLoPAtnZssIF5HALM/EPZBSDGg75Dv+xULjW2Z5ayaoCRBEu4b1pLFc6fxtJFkn6utL/h/npWt\nToOYhZERQkRE+yLKYIsKnnClLlHIWqkKYQnLYIty36NYkXTEPXnZcvmCi/E7DK1SQ1clcroIs2xm\nrRSMbBNcq167b62CvhyCQLLlxBpLYbob5GBtow7zIBETQjOzj/vvBKEa6yUrRyOTAOBaR8rXzLhq\nLgyrFPP252smc8mwKDYtcmkUyb3h9h3FdRdV4Vs0N6GlNCGr4SD6DQY0eQKow7bg4vbLjgzUvGPF\nEbnXhRxwrMiiyJ3GaiM1Z4PXsJ/rgj7IactXRtOSEpaxR4gI1+JFuUDbc+K903UpstY0HaJAk5se\n10V7Ll+RhGDnQVxqecWsQR2yxbMiRQWP5KoQuaTEP3maaDf95RWkbDvg5lGGij5QGayuUMEp/VwX\nHFeeBq6A3r4VM4gUrT1ago4yKK5DZe2iIG6Za4xrHSlfM3vm1Iq13kqZ1eKPIgsOqxSe0B5U+mb+\nH7n+XYIK37KAeBUXMb2P2q7yQfQZBjx5AsqHXPnAIcSCJlEiaw+bCs6C56IKU8uma53R1/A+114j\nVWSXzCGpAGjR4Uz+JrFhf75mciCDjb4e0D/ASXu6Fpyqa5BHbmhJAjZjjcQT6WSqxbEixQGvL9cr\n4u5rp8D1qn/5JiX+KdJEu/Crn4HveXqBxdKBJASIIkm0PlMimkmagbqh1h4NQUcZlPa0KrYnxH0X\nUgKGzgaT3Q/STrrnAguOjjp4Es9VZKHJpBW+2YB4jRJCAVX3gack9F+DLYI8EfBIlIq1R5T1JXJR\nyZ5nNsCXtCUSAYR8hBE3Hnjq1cTNk01bsRXAWZcdLwjaSplV2X9RMuSA4EFN14IT7WHYnhCw5Iv+\n/4Rxw5HXJJqytn31Yuj7JfKUMg24XjFUSiNqkgGvn5zrYeOajzC8wYqf1VQZiO/eCG1TRqJqzApr\nkBE9njWHpNyrjh9WhoYmFsruO9G66HgmooJtN4aSo7A951lwlNXBk3qumPVxoWOtSwokm09hXYPB\n5QMDWxR5IiC/8FWsPWFZX2EuKh54sTwi8hFFtZydf5jrTjUwmnbZqQZBR8mQo9ciqgVH7p+uxUdE\netlA9CRQ6/qILFSlNHjf66jV8/ppa2tDS3NTPKsPgzCNHm6wOI0E0r2VrB0haw5Yc5iUe924G34Z\nmhYusYhdtoQhLEr6Q4zCdxUoC07BccUlWiTK5LVUYI8knZHQmE62Vcmyph1cPojNgi2SPBGoWDDo\ng5VYJNiMOh1Fbrpf3hj0PJJwtYRZHKJqDqkGQYsy5FS1qUS14AgJ0iEmPNILlDS5ACQq9FmrGnlh\nUCGkLFTnKntWbNtGwXEAJPyGLgqoZXSaIlsiQoK8Va0dvDIxVeso/60T3yLrn16/at0/AIECyNJ1\nxdAfCq2HV7ZAkeSaqhgpztg1r+2mYOHT6UtnzFL2rCCmSqJrNhjz1H8xsMuzEISUaVHN2iLlTYj8\nweyZemVYwsCbh6isShJ980q8APx2DWkbhmHA9310FZwAwVNZP90u7prilMKhx857XuR+ZHOLu86k\n5QZUEDZXds8BBNrOueePOPKwQzByqxE1mR+vPEZNyoZEaBPpmhDRxUhzS1mAx1Q3EJQ+qSpDI4OG\nQCQhu9xyKhHWqFVOJaytZsmUqM9Z1PZLVqyjsj3blObT1e1ttvIsOx99CT5YvT6x/rYbPQzvPHpj\nYv1tbmwZlqe8/OAiGV0i8FxPMoHKqIiT9Relb9YqlEnxLQuyeBrV9felNU0G1pKVpKI4a5mJcu+S\ncvXp7mvYXNmEBPb+NTTUo6u7O9acZeAWBY5TxkLRqqQlpaDRbxISCAGdqmwrjPpWONlW8ThUv2nb\nUo+bUSAvqppQovnHGbtq/LDvNS2IWrFWEWQHSmTWq9a8EvTFznHQ+tQ/sWWQpxCEHVhRxBl1EPbs\n19IKkaPcaDJSkySJk8VH6c47ynzoMZNal4gU6ty7pFx9UQlY2FzZZAv6/jU2NKCzsyvahEUQub7K\n4pgkkDkSdNxngjge7oGdUMyX1tzMVDDrUCTkyM6tmMzvL94hr5M9Vovxed8TLSqVGDb631r3U6Kh\nJZt32k5Vk1lZ5mS532xdGk3NWfmcBrFZsMWTJ9UDK0mdHhqig46VAqgleOrdIkuVCDqB6Lxg7ygH\nvuo96Ks9jGvFYvuIgqRjrdjr6YzVgM5U81bo6GTiVmJAZEkgnyeReRVIxdedk0LqvrK1ijMGAHWr\nWtFDwSFacW41KdKZWxSICENf1WcLITiEYN919eSKFlU0/S+1PWPbC61iCmSWmzlJaallsxa+84tF\nSvMaRN9iiydPOode0hYg0UFHiES9bfWrbC0Rwgr48trTwd61DK4OK1WTZEZcElasuMWldZ5nei90\nRTXZfhsaG9HVlZDlSURMqM+FWVpUHyqopOIr1mJTzv7SEP6UjRE2JwI7txF+1wbY+aDAp8g6lghq\nSco41iPhd7LxqT21UiaU6xWyY+numaJgq+q+BYLEKfdoLufilu8fpje3QfQJtjjyxB4YJFBWVVU6\nyTnwDjpCJGZcVTLlnvOzBf0mW4vINLCfhRXwrdrzkMw91QB0lTayUjW1IG2JWSQjluwB1EhcQHuL\nQ5J09yaTyaAnl4ue9UZDYskgn/uuIzx0dIreKluwksj+ChtPw0XElW6wGoKfJamNpDB+UqSM7VtZ\noZ03PrOnVfUKFayAsSQByir4FaugQK8srA9aH4y+n7meAjrac9HnN4iaYYsiT7wDgyhX66hKJzUH\nQFz89a6rJwMAfnPx4YkHpkdB1kqhIW1XHbI84iMjQ6LvVA78KCrltOo2q3fFE/7sLyDzdtxiJEmG\nMBJMEyNRrJZIPoOHQqEAq15Np0YFImLClmepXpwJw7LhFf1SrTVZjJJmPIuopIkywtxLYbpV1Brl\n0g2ZSmZZTbSRakXKeOtgZBXild0J1ivUsgLGkMJI21a5rqoFN90S6WeErMF3nUrBa7KOAZgQ/1+B\nLYY8iQ4MWrm61s+galBx3vMCdaJUNZFqBTJvwzCqLEwi0cSwMjUiTSgRoqiUkyxB3/dhpcwqYUxZ\nrb7NiUwqBStlwvd92JapZRlTIZZVGl0CAsneJ2nfjoOGpsZkDtQwV0kYyUmZeOO99bBSJly7qdKn\nSuZSKGISkMB4QiuYXLeKxPDQBXhdu5HryuzTGKcYoBMA2DggWb09IcKsOwlaAaUo93P7ZUfCyedh\nZ9LRf0bK1k7Sz6BIZv/GFkOe/JwnPDCSzJyTzkHRPZV0Cn1ckPn4vl8la9CQtoXFlZOKH9NRKWdd\nizQRpdXgN5eIZRiq1OY5bl3RdYZRIg5esUQWZWuiiZGM6KpITBgG4HkOVm8oVFwLURHbVVI+rCaM\nGx4kIrIsqL4KbKbmGNkKxsTw0HXnZIKTSSPp8jcyJXNuvT1ObBOB6jMkqwUpzBiM8FIQkJSIS8gG\nRTIHDLYMkUwKRjYVOBBEisthq6bb6IoaRhGW7A+gf06JYOLd105ByjQSFQvlgRVxlAmKul4RrlcM\nWEx4ApBJCZAmDdFaw9aRL7gV16Su6KdIRJVVdRft1wtP/gtbjRqFPfeaAN91ImsvqYgrqqBUsiRd\nLVRIkaW4IpuRIVungtCjazcmLhjKG6cvSWWc+dPXAgbsTLoiOCkT6DQsuxxEHhxTNJdE9jhqADoz\n90GRzP6PLcbyBKCskC0PNlZxe4QF24ahPxEiHZB4Jtoy5vu+UkxMXITFRNGWEcMwAoHWomvzXu/n\nYfPWXZdO+zBNLTqRgLX8qNYYlEE1W1Jqocp3wHK6kOrZGP1gSbQuXhvfQkH6VC1RUgvI1qkQJA5U\nyxgkaQlKJFCaoNb7ylhiiFuMCE6K4uKEljqFTM+oz0tsbbIy2Hudydqx+htEbbDFkKdsXRotQ+oq\n6f88GQDTDHflqATb6iBKHbG4SILgkIO0q+CoxcTEABsbxhuHJXQFxwsEWotq+mVS4QRYd1067UVt\n2fmKXLmqNQZF0M2WFLqai0UY8PnZRBqIRAIEAoRSuYCGoZUsqFoWmhUhdqYeDzqp96rjiAQ3FaBa\nfDgWMWGIaO+/C2L9rvI1s2fyyauI2GoTe9XMzig/L+XxDcNAJmPpXz+ImmOLIE+GYSCbJRkPqUr8\nCy0DQLSH2KBiHmhfeZxsLZlApgopi0KC2HR9GVSUz5Msu8IDT6dJNA4hDt2OU7nPvLnoEGCddYUR\nD157nT3LuR7y5dqKMouQznOomy0pXItpwvM4sTxRoEFkuCn7illUWiVKqOsTAyeIWdZWK1ia05fS\nvaHGKTgujIah0e6lICOQOx82SFyTyPJiogBfulYZeRWJXALVFj8RVGPa4v68+L6PfD6aoO4gaost\ngjz5vo9czsXtlx1ZIUcFx8Ptlx1ZUXLOpi0sWbEOaTsl1Xxi67zRpVpUCYPK2z1dRyyT4gtN6lp5\nSHbZ6yvWhR7Wuv3XIsg9iiaU7wPFYngblWwznXXRliyV9lH0rQwDVXIL9DzZtqrQzZbkjZPJpFFw\n3b51hamm7NPgqTqrCmom6c7S7Vux5Anpx6trDfalYd2xCu3wuzZUSr5EzQyj97lKjDQkSFwbjMUz\ndn1BkcilCjh7zZWh0LS4tbkNVX8AIJ9z1OY1iD7FFkGeACDXU0Dbph50d+YDf+e6ChW9H5KhE6b5\nxHvTVyUbqocskSc47Zp5SkKTYdYp+pq9xg0HAC4pE/WvgqTqxBHE0YQKa6OababSV1U2oCdvTyvI\n0wKtKpY+HSKnG4fHjh9G5uhxrKYRyMGurStMIZ1clLJPgyYhNRHU1F1TSN9s0V1ZX6QfwzCCfenG\nkzESAVHuJV3+RkmDSlBDUGWvlQsTxySBSvvAqUnHlaGQZYAy8yREaRADB7F/Q8ycORNTp07FtGnT\nMGPGDCxdulTY9re//S2++MUvYtKkSbj11lvjDl0FkjjI/o28hx7X07IC0N/ruNlUD1kdoUkiG8Ae\nluRwqytLCdDWLNlcN4dUgmjPomhC8dqI1qnaX5jsAs/1xQN5BmgXcoajfi4Ct64ch1izyvRh5FqX\ncLHP8lYjhmHm756I5gpTgIjksLpJ0pR9GmVLhYi0xBXU1FqTzG0VUQXd9/2qvpTirOKWXOHpKwn2\nTiQFoKwoTl2jUpi4lpZDFqyYq+j+8lyEXl1JbDZvtwYsTIPQRy6Xw0UXXYRJkybh6KOPxqJFi6Tt\nC4UCjj76aHzlK18J9HHxxRfjuOOOw3HHHYfvfe976O7uDh07Nnk69NBD8cgjj+DBBx/E2WefjYsu\nuojb7qWXXsL8+fMxZ84cPPzww3jsscfw0ksvxR1eHfleC4SuKGUc/SbZoawiNJn3PGkW1pIV65Ap\nx8iQa8ibuWyu7NgqJUKiBoyHXSeSk+CB93lY8WVZH6pWtzBrE+mLPAO0CzlHSQyoxEmx8gH02nwf\nFZd0wfGkVlGaXOtaGtlnuXXYCNx06fHBAqeC2BttKLjiyN/CQGDuIuhDrVexWSqomRQxVHVb6aqg\nl/sRZj1qlnyJG39WmRdLZjgp+7yiz6LMN3ZNoYWJo1oOYwazkz6kRJR57ipixFlrUMcpJu644w40\nNjZi/vz5uO2223DFFVegp6dH2P6Xv/wl9t1338Bn9957L1zXxcMPP4yHH34YruvinnvuCR07dhj/\noYceWvn33nvvjTVr1nDbPfroo5g2bRrS6TQAYNq0aZg7dy7222+/uFNQR95Dpi6NbNZCLuci11NQ\nvjSX95D//+2dfUxT1xvHv22h5UVBahwUWNjilrjBEkY2N2Uy5aUtykvLMqkzMUMyXyZjMSYbMbgI\nugxYfjMKW7IYFpcfxMiWDW2wbobAzwmLsJH9wWQJZMEXis4oL25SoPT8/gCulL7d8tL2ds8nuWnv\nPc+55/me59xzz729PffRpMup8p3ZicViWCzWndV8qzVrVuLevYdWaSbRzEnMZAYzTXFp4+LHP0We\nPZbJlWsCP19nU0NWyCANlGBicgqP/h63aysSiRAUIrMpyxXW+bbyyhfkIEbzt892PPb84rMPALza\ngtW+TNZ209NjzJYXCJNpEqZ/Jjg/xmbSYYFNDF3ptlfnACCWiTBlYRCLRRBPMgSFBNjon5/XNG7b\nhlwxty0HioPw4ouJM20zlPPVPGWBedLi1rFkV7vpsX+myWCH9RMQMH1ym2IS/MPnit08hWCYIZNJ\nYWIRnJ/2ylsZFgyJROR237AYTbM+isbGZo4Lx5octen5zLbJ+dvCZUHYceTizBxhK3gdv9b5H7cn\nZ/nt+Tk3/3+Pqrh6AWBVRw41uqijNWtW4uHf4/zqe76/fOPEU6sz24CZCTu5Y1Go89r4CAaDAZWV\nlQCAuLg4JCQk4MqVK1CpVDa2v/zyC27cuIGCggL88ccf3HaRSASTyYTJyZk+e2wMUVFRLste0mee\n6urqsHnzZrtpRqMR0dHR3LpCocDg4KDDfY2OjuL27dtWy6y9WCxa0CKRiBEUFIDi/7QiKCgAEonY\nrfwiEb+y7dmFrpQhLDwIoStlTvPa0zcxPomHoyZMjE9abR83TXL/BhwfN1uVy9fXgAAJpIESFP+n\nFdJACQICJA41jY/bL8tVXczms0xZXOZzFKP520NCp6emCAoOtPGL7z74tAVnbSY4ZNqH4FDpjE0L\nZzOrc/bTUQydleOozgMkYhw88T9ulnF7NvO3T5icl8+nLc+2zbm+imf+6eruscS3jc+vH7FI5Nbx\nK5GIIZPZxm9+eRKJGBKJeMF9w0I0Oapnd9vh3IVrkyFSm/1bZu5Iu3P8unv8O/Jzbv4ps4Wrl7l1\n5EqjM58BuFXfC43TQmIy1/bQySsIkIjx98Nxt8oDgMHBQZtz4ujo8k3+GhMpR5xi9ZItMZHyJdfB\nd1wxNjaGTz75BGVlZTYDVp1Oh5CQECQnJ2PTpk0ICwvDtm3bXJbtcobxvLw8G2dmr2zb29u5q/+m\npibU1NSgvr4ecrncZj/79u2DVqvlRoQGgwF6vd7hs0/V1dWoqamx2paUlMTrdhpBEARB+BM7duxA\nV1eX1baioiK89957XvLIfUwmE1JSUjAyMmK13ZEOZ+OPtrY2vPTSS2hubkZERAQAoKysDHFxcXj7\n7bet8pSVleGFF15AXl4erl27hk8//RTffvstAKC1tRV6vR4VFRVgjOHQoUNISkpCQUGBczFsCfjx\nxx9ZRkYGMxqNDm3KysrYV199xa3X1tay8vJyh/YjIyPs1q1bVktnZyfT6XROyxEyRqORbdmyxS/1\n+bM2xkif0CF9wsWftTE2rU+n07HOzk6bc+LIyIi33XMLe+f1xejIyspi3d3d3PrevXvZpUuXbOyy\ns7NZamoqS01NZcnJySwhIYHl5ORweQwGA2fb1NTE9u7d67LsRT/z1NLSgoqKCpw5cwYKhcKhnVqt\nxscff4ydO3fCYrGgsbERH330kUP7sLAwhIXZPpjY1dWFKTcf+BYKU1NTGBgY8Et9/qwNIH1Ch/QJ\nF3/WBkzr6+rqQlRUFGJjY73tzqJwdF5fKCqVCufOnUN5eTn6+/vR3d2Nzz77zMbuwoUL3PeOjg5U\nVVVxd55iY2Nx9epVqNVqWCwW/PTTT3j22Wddlr3oZ54OHz4Ms9mM4uJiaDQaaLVa7pZcaWkpWlpa\nAADr169HRkYGtm3bhuzsbKhUKs8+LE4QBEEQhN9QWFiIkZERKJVK7N+/H8eOHUNISAgA4NSpUzh3\n7pzLfRw4cAAjIyPIyspCbm4uJicnsW/fPpf5Fn3n6eeff3aYdvz4cav1oqIiFBUVLbZIgiAIgiD+\n5QQHB+PkyZN204qLi+1uX79+PXfXCQAiIiJQXV3tdtl+M8M4QRAEQRCEJ5AcPXr0qLedcAeZTIZX\nXnkFMr7vIRIY/qzPn7UBpE/okD7h4s/aAP/XJ0RcTlVAEARBEARBPIZ+tiMIgiAIgnADGjwRBEEQ\nBEG4AQ2eCIIgCIIg3MDnB0/l5eXIzMyERqPBW2+9he7uboe2n3/+OTIyMqBUKh2+9sXXuHDhAnJy\nchAfH4/6+nqHdh0dHUhMTIRWq4VGo0F+fr4HvVwYfLUBQENDA5RKJZRKpc0UF76KyWTCwYMHoVQq\nsXXrVrS2ttq1E1Ls+vv7odPpoFarodPpcPPmTRsbi8WCsrIyZGRkQKVS4ZtvvvGCpwuDj76amhps\n3LgRWq0WWq0Wx44d84Kn7lNZWYm0tDSsW7cOfX19dm2EHDs++oQau+HhYezZsweZmZnIzc1FcXEx\nhoaGbOz49jmEB1jQnOgepLW1lZnNZsYYYy0tLSw9Pd2uXWdnJ8vJyWHj4+PMZDKx7Oxs1tnZ6UlX\nF0Rvby/r6+tjH374Iaurq3Nod+3aNfbGG2940LPFw1fbrVu3WEpKChsaGmKMMbZ7927W2NjoKTcX\nTE1NDSstLWWMMdbf38+Sk5PZo0ePbOyEFLtdu3YxvV7PGGPs/PnzbNeuXTY233//PSssLGSMMXb/\n/n2WkpLCBgYGPOrnQuGjr7q6mlVWVnratUXz66+/sjt37rDU1FTW29tr10bIseOjT6ixGx4eZh0d\nHdx6ZWUlO3z4sI0d3z6HWH58/s7T66+/DolEAgBITEzE3bt37dpdvHgRGo0GUqkUMpkMGo0GBoPB\nk64uiGeeeQZr167lXrDsDCawP0by1fbDDz8gIyMDq1atAgBs375dELEzGAzQ6XQAgLi4OCQkJODK\nlSt2bYUQuwcPHqCnp4d7o3hWVhauX79ucwVsMBiwfft2AIBcLkd6ejouXbrkcX/dha8+QBjxmk9S\nUhIiIyOd+i7U2AH89AHCjF14eDhefvllbj0xMdHmhbiAe30Osbz4/OBpLnV1ddi8ebPdNKPRiOjo\naG5doVDYbXxC5saNG8jLy0N+fj4aGxu97c6SMTg4KMjYudPmhBC7wcFBREZGcoNdsViMJ554Anfu\n3LGyE+qxxlcfMH2Sys3NRWFhIX777TdPu7psCDV27iD02DHGcPbsWaSlpdmk/RviJxQW/XqWxZKX\nl2cTfMYYRCIR2tvbuY6uqakJTU1NLp+d8TX46nNFfHw8WltbsWLFCty+fRsFBQWIjIzEhg0blsNt\nXiyVNl/Fmb62tjbe+/HF2BGO2bFjB/bv3w+JRIL29na8++67MBgMCA8P97ZrhAv8IXbl5eUIDQ3F\nzp07bdKE3qf6E14fPH333XcubS5fvoyTJ0/i66+/hlwut2sTHR0No9HIrQ8ODkKhUCyZnwuFjz4+\nhIaGct9jY2ORnp6Orq4ur56Al0qbQqHAwMAAty6U2MXExMBoNCIiIgLAtN+vvvqqjZ0vxs4eCoUC\nd+/e5QaIFosFf/31F6KioqzsZo+1hIQEANO6Y2JivOGyW/DVt3r1au77xo0bERUVhd7eXr94kblQ\nY8cXoceusrISN2/exJdffmk3fTZ+rvocYvnx+Z/tWlpaUFFRgdraWqcnVLVajcbGRkxMTMBkMqGx\nsRGZmZke9HR5uXfvHvd9eHgYV69exXPPPedFj5YOpVKJ5uZmDA0NwWKxoKGhAWq12ttuuUSlUnFv\n7e7v70d3dzc2bdpkYyeU2Mnlcqxbtw56vR4AoNfr8fzzz3Md9SxqtRoNDQ1gjOHBgwdobm6GUqn0\nhstuwVff3Ocqe3p6YDQa8fTTT3vU1+VCqLHji5Bjd+LECVy/fh1ffPEFAgLs39fg2+cQy4/Pv55l\nw4YNkEqlkMvl3BXjmTNnEB4ejtLSUqSlpWHLli0Apv+mev78eQCARqPBgQMHvOk6L5qamlBVVYXR\n0VFIpVIEBwejtrYWa9euxalTpxAZGYn8/HzU19fj7NmzCAwMhNlshlarxe7du73tvlP4agOmpyo4\nffo0RCIRXnvtNRw5csTnb1GPjY2hpKQEPT09kEgk+OCDD7i2KNTY/fnnnygpKcHo6CjCw8NRVVWF\nuLg47NmzB++//z7i4+NhsVhQXl6OtrY2iEQivPPOO3jzzTe97Tov+OgrKSnB77//DrFYDKlUiuLi\nYkGcoI4fP47Lly/j/v37WLVqFSIiIqDX6/0mdnz0CTV2fX19yM7OxlNPPcW9v+7JJ59EdXU1NBoN\nTp8+jTVr1jjtcwjP4vODJ4IgCIIgCF/C53+2IwiCIAiC8CVo8EQQBEEQBOEGNHgiCIIgCIJwAxo8\nEQRBEARBuAENngiCIAiCINyABk8EQRAEQRBuQIMngiAIgiAIN/g/hWLfhf9T7/kAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f08abcad290>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "zi = griddata(x, y, z, xi, yi, interp='linear')\n",
    "plot_contour(xi, yi, zi)\n",
    "plt.scatter(df.x, df.y, marker='.')\n",
    "plt.title('Contour on training data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hoANr0f2GFrM"
   },
   "outputs": [],
   "source": [
    "fc = [tf.feature_column.numeric_column('x'),\n",
    "      tf.feature_column.numeric_column('y')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xVRWyoY3ayTK"
   },
   "outputs": [],
   "source": [
    "def predict(est):\n",
    "  \"\"\"Predictions from a given estimator.\"\"\"\n",
    "  predict_input_fn = lambda: tf.data.Dataset.from_tensors(dict(df_predict))\n",
    "  preds = np.array([p['predictions'][0] for p in est.predict(predict_input_fn)])\n",
    "  return preds.reshape(predict_shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uyPu5618GU7K"
   },
   "source": [
    "First let's try to fit a linear model to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zUIV2IVgGVSk"
   },
   "outputs": [],
   "source": [
    "train_input_fn = make_input_fn(df, df.z)\n",
    "est = tf.estimator.LinearRegressor(fc)\n",
    "est.train(train_input_fn, max_steps=500);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 502
    },
    "colab_type": "code",
    "id": "_u4WAcCqfbco",
    "outputId": "c67fa79f-319a-4c80-e2d2-dacf439133c6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ERx99hB07duCpp57Chg0b4PWOrsXE3m1bR/X3n42uzg68upPjecbytmttacae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\ndTXSh2gy3rRpEzZs2DDgtby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KRlJ8KkWb5W2XkpKKpDjOTZXlbSext41p28Hd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\nEgRBEARBaAX/C6r7iNk12uGzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f08ab457550>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_contour(xi, yi, predict(est))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XD_fMAUtSCSa"
   },
   "source": [
    "It's not a very good fit. Next let's try to fit a GBDT model to it and try to understand how the model fits the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 523
    },
    "colab_type": "code",
    "id": "-dHlKFlFgHDQ",
    "outputId": "3d916f4f-834b-4567-addd-c101c96d4c1e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8Ucft26sHjhw/iZ7d7mrUcQghhBAiDEqeasHzPB7/279cf2BVOAxVJQBo1IkQQgjxRnTZ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\n/ylMBXehJVQ0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f08acb936d0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_trees = 22 #@param {type: \"slider\", min: 1, max: 80, step: 1}\n",
    "\n",
    "est = tf.estimator.BoostedTreesRegressor(fc, n_batches_per_layer=1, n_trees=n_trees)\n",
    "est.train(train_input_fn, max_steps=500)\n",
    "clear_output()\n",
    "plot_contour(xi, yi, predict(est))\n",
    "plt.text(-1.8, 2.1, '# trees: {}'.format(n_trees), color='w', backgroundcolor='black', size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5WcZ9fubh1wT"
   },
   "source": [
    "As you increase the number of trees, the model's predictions better approximates the underlying function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cj8u3NCG-IKX"
   },
   "source": [
    "![](https://www.tensorflow.org/images/boosted_trees/boosted_trees_ntrees.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SMKoEZnCdrsp"
   },
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZSZUSrjXdw9g"
   },
   "source": [
    "In this tutorial you learned how to interpret Boosted Trees models using directional feature contributions and feature importance techniques. These techniques provide insight into how the features impact a model's predictions. Finally, you also gained intution for how a Boosted Tree model fits a complex function by viewing the decision surface for several models."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "boosted_trees_model_understanding.ipynb",
   "toc_visible": true,
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
